基于改进的GM(1,1)-Markov链组合模型广东省单位GDP能耗预测

本文以灰色系统理论的GM(1,1)模型和随机过程理论的Markov链模型为基础构建了一个动态GM(1,1)-Markov链组合预测模型。该模型同时利用了GM(1,1)模型对序列趋势因素良好的拟合能力和Markov链模型对残差序列信息的提取能力。为进一步提高该模型的预测精度,用泰勒(Taylor)近似方法和新信息优先的思想对该模型进行了改进。最后,以1991-2014年广东省单位GDP能耗数据实证了该模型的预测效果。

An Improved GM(1,1)-Markov Chain Combined Model with an Application to Predict the Energy Consumption Per Unit of GDP in Guangdong Province

In this paper,we propose a new dynamic model which combines first-order one-variable grey differential equation model (abbreviated as GM(1,1) model) from grey system theory and Markov chain model from stochastic process theory.This combined model takes advantage of the high predictable power of GM(1,1) model for tendency prediction and at the same time takes advantage of the prediction power of Markov chain modelling on the GM(1,1) modelling residual sequence.For prediction accuracy improvement,Taylor approximation and the idea of new information priority have applied to the combined model.As an example,we use the statistical data of the energy consumption per unit of GDP in Guangdong province from 1991 to 2014 for a validation of the effectiveness of the combined model.